search for: invwt

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance structure (eg varPower, etc..), preferably using an lme object of the varFunc classes ? Some tria...

...Xr7 Xr8 Residual StdDev: 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 0.0004377781 1.693177 Correlation Structure: AR(1) Formula: ~1 | g Parameter estimate(s): Phi 0.3110725 Variance function: Structure: fixed weights Formula: ~invwt Number of Observations: 264 Number of Groups: 1 $gam Family: binomial Link function: logit Formula: y ~ s(xc) + z + int Estimated degrees of freedom: 1 total = 4 attr(,"class") [1] "gamm" "list" **************************** > g2 $lme Linear mixed-effects mode...

..., 2.27586790674987, -2.49392989051313, 2.27586790674987, 3.09612363937901, 2.27586790674987, 3.09612363937901, 2.27586790674987, -2.49392989051313, 2.27586790674987, -2.49392989051313, -2.48603224779624, 3.09612363937901, 2.27586790674987, -2.49392989051313), invwt = c(5.11362806892812, 4.49999834749486, 5.11362806892812, 4.49999834749486, 5.11362806892812, 4.49999834749486, 5.11362806892812, 4.49999834749486, 5.11362806892812, 4.49999834749486, 5.11362806892812, 4.49999834749486, 5.11362806892812, 4.49999834749486, 5.113...

...lmmPQL) Linear mixed-effects model fit by maximum likelihood Data: fernando AIC BIC logLik 30.51277 49.25655 -9.256384 Random effects: Formula: ~1 | subject (Intercept) Residual StdDev: 8.284993 4.113725e-09 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: ifelse(class == "Disease", 1, 0) ~ age + x1 + x2 Value Std.Error DF t-value p-value (Intercept) -35.01862 2.4414559 123 -14.3 0 age 0.59026 0.0441817 123 13.4 0 x1 1.39317 0.0000014 41 1000507.2 0 x...

...by maximum likelihood Data: wc2 Log-likelihood: NA Fixed: LOSS ~ YR + offset(log(PR)) (Intercept) YR -4.2830507147 0.0005085944 Random effects: Formula: ~1 | CL (Intercept) Residual StdDev: 0.8313193 0.5346455 Variance function: Structure: fixed weights Formula: ~invwt Number of Observations: 700 Number of Groups: 100 > fit2 Error in asMethod(object) : matrix is not symmetric [1,2] Wayne (Yanwei) Zhang Statistical Research CNA Email: Yanwei.Zhang@cna.com<mailto:Yanwei.Zhang@cna.com> NOTICE: This e-mail message, including any attachments and appe...

...ummy.glmm20) Linear mixed-effects model fit by maximum likelihood Data: dummy20 AIC BIC logLik 10270.78 10293.19 -5131.392 Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 50.55603 0.7928198 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ x Value Std.Error DF t-value p-value (Intercept) -88.62246 11.686506 1979 -7.583316 <.0001 x 0.08768 0.002913 1979 30.099686 <.0001 Correlation: (Intr) x -0.252 Standardized Within-Group Residuals: Min Q1 Med...

...063304 0.0001063304 0.0001063304 Xr.46 Xr.47 Xr.48 StdDev: 0.0001063304 0.0001063304 0.0001063304 Formula: ~1 | fac %in% g.4 %in% g.3 %in% g.2 %in% g.1 (Intercept) Residual StdDev: 0.6621173 1.007227 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y.0 ~ X - 1 Value Std.Error DF t-value p-value X(Intercept) 2.0870364 0.3337787 392 6.252755 0.0000 Xs(x0)Fx1 -0.0000325 0.1028794 392 -0.000316 0.9997 Xs(x1)Fx1 0.3831694 0.0957323 392 4.002509 0.0001 Xs(x2)Fx1 1.4584330 0.3909237 392 3.73073...

...ion 3 iteration 4 iteration 5 iteration 6 Linear mixed-effects model fit by maximum likelihood Data: syllogisms AIC BIC logLik NA NA NA Random effects: Formula: ~1 | subject (Intercept) Residual StdDev: 1.817202 0.8045027 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: accuracy ~ f_power * f_type Value Std.Error DF t-value p-value (Intercept) 1.1403334 0.4064642 599 2.805496 0.0052 f_powerhp 0.0996481 0.5683296 83 0.175335 0.8612 f_powerlow 1.5358270 0.6486150...

...lmmPQL.fit)$tTable Linear mixed-effects model fit by maximum likelihood Data: df AIC BIC logLik 1800.477 1840.391 -890.2384 Random effects: Formula: ~1 | subject (Intercept) Residual StdDev: 0.6355517 0.9650671 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: score ~ x * type Value Std.Error DF t-value p-value (Intercept) -0.0812834 0.2933314 294 -0.2771043 0.7819 x1 0.4143072 0.4180624 98 0.9910176 0.3241 type2 0.8509166 0.4084443 294 2.0833112 0.0381 type3 0.6691275 0.4024369 294 1.662...

...1 | ID,family = binomial, data = bacteria)) Linear mixed-effects model fit by maximum likelihood Data: bacteria AIC BIC logLik NA NA NA Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.410637 0.7800511 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: y ~ trt + I(week > 2) Value Std.Error DF t-value p-value (Intercept) 3.412014 0.5185033 169 6.580506 0.0000 trtdrug -1.247355 0.6440635 47 -1.936696 0.0588 trtdrug+ -0.754327 0.6453978 47...

...ons: 20 iteration 1 > summary(glm1.gamma$lme) Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 847.722 866.241 -418.861 Random effects: Formula: ~1 | random1 (Intercept) Residual StdDev: 2.954058e-05 0.9775214 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: list(fixed) Value Std.Error DF t-value p-value X(Intercept) 5.066376 0.08363392 295 60.57801 0e+00 Xx1TRUE 0.884486 0.11421762 295 7.74387 0e+00 Xx2 0.234537 0.05851689 295 4.00802 1e-04 Correlation: X(Int) X1TRUE Xx1TRUE -0.733 Xx2 -0.008 0.085 Standardized Within-Group Residuals:...

...del fit by maximum likelihood Data: p5 AIC BIC logLik NA NA NA Random effects: Formula: ~1 | p (Intercept) StdDev: 0.1165222 Formula: ~1 | aa %in% p (Intercept) Residual StdDev: 0.6551498 0.9735705 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: Y ~ 1 Value Std.Error DF t-value p-value (Intercept) -0.1256839 0.1117682 938 -1.124505 0.2611 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.4693363 -0.8816572 -0.5038361 0.9541089 2.0939872 Number of Observa...

...AIC BIC logLik 35838.51 35864.14 -17915.26 Random effects: Formula: ~1 | groupid (Intercept) StdDev: 0.9113356 Formula: ~1 | participantid %in% groupid (Intercept) Residual StdDev: 1.222692 0.8411629 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: r.info.doubt ~ 1 Value Std.Error DF t-value p-value (Intercept) -5.523606 0.3170107 4351 -17.42404 <.0001 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -0.54548030 -0.06825715 -0.05379631 -0.04567625 18.29...

...t by maximum likelihood Data: dados AIC BIC logLik NA NA NA Random effects: Formula: ~1 | animal (Intercept) StdDev: 0.4235518 Formula: ~1 | idfid %in% animal (Intercept) Residual StdDev: 0.947683 1.752526 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: allclues ~ cycloc + male Value Std.Error DF t-value p-value (Intercept) 1.8050720 0.2653779 333 6.801892 0.0000 cycloc 0.0718826 0.0128099 181 5.611469 0.0000 male1 -0.6254748 0.3552453 5 -1.760684 0.1386 Correlation: (Intr) cycloc c...

...e following results: Linear mixed-effects model fit by maximum likelihood Data: new AIC BIC logLik 53238.5 53260.74 -26616.25 Random effects: Formula: ~1 | GRUPO (Intercept) Residual StdDev: 0.3402137 0.9952645 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: POS ~ 1 Value Std.Error DF t-value p-value (Intercept) 0.6214472 0.03723763 12025 16.68869 0 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.7831032 -1.2642113 0.6508504 0.7058691 1.1945426 Number of O...

...mily=negative.binomial(theta)) > summary(m1) Linear mixed-effects model fit by maximum likelihood Data: a AIC BIC logLik NA NA NA Random effects: Formula: ~1 | BUSHID (Intercept) Residual StdDev: 1.081722 0.6577578 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: MANUAL ~ BERRIES Value Std.Error DF t-value p-value (Intercept) 7.602133 0.23105575 91 32.90172 0 BERRIES 0.001369 0.00023395 28 5.85324 0 Correlation: (Intr) BERRIES -0.485 Standardized Within-Group Residuals: Min Q1...

...1.glmmpql) Linear mixed-effects model fit by maximum likelihood Data: fish.data AIC BIC logLik 1236.652 1255.103 -612.326 Random effects: Formula: ~1 | Tank (Intercept) Residual StdDev: 0.02001341 0.8944214 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: dead ~ Parasite * Bacteria Value Std.Error DF t-value p-value (Intercept) -18.56607 1044.451 150 -0.01777591 0.9858 Parasiteyes 15.85802 1044.451 6 0.01518311 0.9884 Bacteriayes 17.23107 1044.451 6 0.0...

...: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 0.27914612 (Intr) thick thick 0.03089333 -0.251 I(thick^2) 0.01867846 -0.878 -0.067 Residual 1.37952490 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: lixt ~ thick + I(thick^2) Value Std.Error DF t-value p-value (Intercept) -4.038800 0.4834792 446 -8.353617 0 thick 2.371842 0.2642474 446 8.975837 0 I(thick^2) -0.279189 0.0337451 446 -8.273479 0 Correlation: (Intr) thick...

...AIC BIC logLik 4427.735 4447.531 -2209.868 Random effects: Formula: ~1 | groupid (Intercept) StdDev: 0.3312237 Formula: ~1 | participantid %in% groupid (Intercept) Residual StdDev: 0.3651775 0.9765288 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: r.logic.morality ~ 1 Value Std.Error DF t-value p-value (Intercept) -0.1699931 0.1039887 905 -1.634727 0.1025 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.2951648 -0.8865510 -0.7183326 1.0428044 1.6135857...

...4) iteration 1 > summary(apfa.glmm.1) Linear mixed-effects model fit by maximum likelihood Data: apfa4 AIC BIC logLik NA NA NA Random effects: Formula: ~1 | Territory (Intercept) Residual StdDev: 0.0003431913 1.051947 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: Success ~ MeanAge + I(MeanAge^2) Value Std.Error DF t-value p-value (Intercept) 5.559466 0.6416221 624 8.664705 0.0000 MeanAge -0.090824 0.0429346 624 -2.115397 0.0348 I(MeanAge^2) 0.001493 0.0006436 624 2.319090 0.0207 Correlation: (Int...